With the system set up on one channel, Cendant has already seen a benefit. During initial testing, the tool revealed that a few-hundred-thousand rate plans had no inventory allocated to them. As a result, requests against those plans received an error code. "We kept telling them the product is not available," says Forte -- a costly error. The dashboard picked up on the problem, allowing staffers to quickly remedy it and limit a potentially large loss of revenue.
The next step will be to expand the system to all of Cendant's channels, Forte says. He also hopes to use the system to automate yield management. "If we see occupancy rates going up on a property, we might want to trigger an event to send rates higher by some percentage," he says.
Forte describes stream processing and Cendant's move to an SOA as the first steps toward a more proactive approach to operations. "The wave of the future is predictive modeling," he says. But that's in the future. Right now, Forte says, "we're trying to get all of the plumbing into place."
The biggest challenge to stream processing may not be the technology but the change in mind-set that's required to effectively use the tools. "The barrier is changing the way you think about the problem," says Tibbling. "In this case, it's how you think about business problems in multiple dimensions. How do you externalize what your brain does automatically? To put that in software is a difficult matter."
Stream processing tools play by the rules
The secret to using stream processing tools effectively lies in knowing what you want to monitor for and creating the proper rules. The tools for creating queries fall into two competing camps: rules engines built on if/then statement logic and variants of SQL, which have been extended to support time-based queries. Mike Stonebreaker, chief technology officer at StreamBase Systems, prefers the latter. "SQL is great paradigm. People understand it and what you do is adapt SQL to real-time streaming data," he says. Other vendors that use SQL include Aleri Labs and Celequest Corp. "The problems with rule engines is there's no standard; there's no rule equivalent of SQL," he says. But while SQL is a standard, a variant of SQL with extensions to support stream-based queries doesn't exist. Stonebreaker also criticizes rules engine syntax, which he says is overly complex. "Rule languages have problems of inscrutable notation, and it's completely unordered," he says. If anyone was going to support SQL, one would think it would be Progress Software, but the database vendor favors the rules engine that came with its acquisition of Apama. "The important part here is to have a robust enough language to express temporal logic. Time as the first order issue as opposed to filtering," says Mark Palmer, vice president of event stream processing at Progress' real time division.
At a glance: stream processing
What it does: Stream processing software monitors and analyzes operational data flows in real time to detect predetermined conditions or events.
Pros: Can handle very large volumes of streaming data with very low latency requirements; faster than custom-built applications; integrates well with service-oriented architectures.
Cons: Most applications are still in financial services; products are relatively new and still maturing; analysts expect a vendor shakeup in the next two years; capabilities may eventually be embedded within existing tools such as databases and business activity monitoring.
Best Use: Applications that require fast detection of and reaction to business events embedded in large volumes of data, such as fraud detection, network monitoring or transaction monitoring.